pacman::p_load(olsrr, corrplot, ggpubr, sf, spdep, GWmodel, tmap, tidyverse, gtsummary)Hands-on Exercise 11:[Cont.] GWR
Reusing derived data from previous section,
condo_resale.sp = read_rds('data/rds/condo_resale_sp.rds')
mpsz_3414 = read_rds('data/rds/mpsz_3414.rds')Building Fixed Bandwidth GWR Model
# compute fixed BW using bw.gwr() from GWModel
bw.fixed <- bw.gwr(
formula = SELLING_PRICE ~
AREA_SQM + AGE + PROX_CBD + PROX_CHILDCARE +
PROX_ELDERLYCARE + PROX_URA_GROWTH_AREA +
PROX_MRT + PROX_PARK + PROX_PRIMARY_SCH +
PROX_SHOPPING_MALL + PROX_BUS_STOP + NO_Of_UNITS +
FAMILY_FRIENDLY + FREEHOLD,
data=condo_resale.sp,
approach="CV", # cross-validation
kernel="gaussian",
adaptive=FALSE,
longlat=FALSE)Fixed bandwidth: 17660.96 CV score: 8.259118e+14
Fixed bandwidth: 10917.26 CV score: 7.970454e+14
Fixed bandwidth: 6749.419 CV score: 7.273273e+14
Fixed bandwidth: 4173.553 CV score: 6.300006e+14
Fixed bandwidth: 2581.58 CV score: 5.404958e+14
Fixed bandwidth: 1597.687 CV score: 4.857515e+14
Fixed bandwidth: 989.6077 CV score: 4.722431e+14
Fixed bandwidth: 613.7939 CV score: 1.379526e+16
Fixed bandwidth: 1221.873 CV score: 4.778717e+14
Fixed bandwidth: 846.0596 CV score: 4.791629e+14
Fixed bandwidth: 1078.325 CV score: 4.751406e+14
Fixed bandwidth: 934.7772 CV score: 4.72518e+14
Fixed bandwidth: 1023.495 CV score: 4.730305e+14
Fixed bandwidth: 968.6643 CV score: 4.721317e+14
Fixed bandwidth: 955.7206 CV score: 4.722072e+14
Fixed bandwidth: 976.6639 CV score: 4.721387e+14
Fixed bandwidth: 963.7202 CV score: 4.721484e+14
Fixed bandwidth: 971.7199 CV score: 4.721293e+14
Fixed bandwidth: 973.6083 CV score: 4.721309e+14
Fixed bandwidth: 970.5527 CV score: 4.721295e+14
Fixed bandwidth: 972.4412 CV score: 4.721296e+14
Fixed bandwidth: 971.2741 CV score: 4.721292e+14
Fixed bandwidth: 970.9985 CV score: 4.721293e+14
Fixed bandwidth: 971.4443 CV score: 4.721292e+14
Fixed bandwidth: 971.5496 CV score: 4.721293e+14
Fixed bandwidth: 971.3793 CV score: 4.721292e+14
Fixed bandwidth: 971.3391 CV score: 4.721292e+14
Fixed bandwidth: 971.3143 CV score: 4.721292e+14
Fixed bandwidth: 971.3545 CV score: 4.721292e+14
Fixed bandwidth: 971.3296 CV score: 4.721292e+14
Fixed bandwidth: 971.345 CV score: 4.721292e+14
Fixed bandwidth: 971.3355 CV score: 4.721292e+14
Fixed bandwidth: 971.3413 CV score: 4.721292e+14
Fixed bandwidth: 971.3377 CV score: 4.721292e+14
Fixed bandwidth: 971.34 CV score: 4.721292e+14
Fixed bandwidth: 971.3405 CV score: 4.721292e+14
Fixed bandwidth: 971.3396 CV score: 4.721292e+14
Fixed bandwidth: 971.3402 CV score: 4.721292e+14
Fixed bandwidth: 971.3398 CV score: 4.721292e+14
Fixed bandwidth: 971.34 CV score: 4.721292e+14
Fixed bandwidth: 971.3399 CV score: 4.721292e+14
Fixed bandwidth: 971.34 CV score: 4.721292e+14 # use recommend BW and gaussian kernel to calibrate GWR mode
gwr.fixed <- gwr.basic(
formula = SELLING_PRICE ~
AREA_SQM + AGE + PROX_CBD + PROX_CHILDCARE +
PROX_ELDERLYCARE + PROX_URA_GROWTH_AREA +
PROX_MRT + PROX_PARK + PROX_PRIMARY_SCH +
PROX_SHOPPING_MALL + PROX_BUS_STOP + NO_Of_UNITS +
FAMILY_FRIENDLY + FREEHOLD,
data=condo_resale.sp,
bw=bw.fixed,
kernel = 'gaussian',
longlat = FALSE)
gwr.fixed ***********************************************************************
* Package GWmodel *
***********************************************************************
Program starts at: 2024-10-28 00:10:00.104743
Call:
gwr.basic(formula = SELLING_PRICE ~ AREA_SQM + AGE + PROX_CBD +
PROX_CHILDCARE + PROX_ELDERLYCARE + PROX_URA_GROWTH_AREA +
PROX_MRT + PROX_PARK + PROX_PRIMARY_SCH + PROX_SHOPPING_MALL +
PROX_BUS_STOP + NO_Of_UNITS + FAMILY_FRIENDLY + FREEHOLD,
data = condo_resale.sp, bw = bw.fixed, kernel = "gaussian",
longlat = FALSE)
Dependent (y) variable: SELLING_PRICE
Independent variables: AREA_SQM AGE PROX_CBD PROX_CHILDCARE PROX_ELDERLYCARE PROX_URA_GROWTH_AREA PROX_MRT PROX_PARK PROX_PRIMARY_SCH PROX_SHOPPING_MALL PROX_BUS_STOP NO_Of_UNITS FAMILY_FRIENDLY FREEHOLD
Number of data points: 1436
***********************************************************************
* Results of Global Regression *
***********************************************************************
Call:
lm(formula = formula, data = data)
Residuals:
Min 1Q Median 3Q Max
-3470778 -298119 -23481 248917 12234210
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 527633.22 108183.22 4.877 1.20e-06 ***
AREA_SQM 12777.52 367.48 34.771 < 2e-16 ***
AGE -24687.74 2754.84 -8.962 < 2e-16 ***
PROX_CBD -77131.32 5763.12 -13.384 < 2e-16 ***
PROX_CHILDCARE -318472.75 107959.51 -2.950 0.003231 **
PROX_ELDERLYCARE 185575.62 39901.86 4.651 3.61e-06 ***
PROX_URA_GROWTH_AREA 39163.25 11754.83 3.332 0.000885 ***
PROX_MRT -294745.11 56916.37 -5.179 2.56e-07 ***
PROX_PARK 570504.81 65507.03 8.709 < 2e-16 ***
PROX_PRIMARY_SCH 159856.14 60234.60 2.654 0.008046 **
PROX_SHOPPING_MALL -220947.25 36561.83 -6.043 1.93e-09 ***
PROX_BUS_STOP 682482.22 134513.24 5.074 4.42e-07 ***
NO_Of_UNITS -245.48 87.95 -2.791 0.005321 **
FAMILY_FRIENDLY 146307.58 46893.02 3.120 0.001845 **
FREEHOLD 350599.81 48506.48 7.228 7.98e-13 ***
---Significance stars
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 756000 on 1421 degrees of freedom
Multiple R-squared: 0.6507
Adjusted R-squared: 0.6472
F-statistic: 189.1 on 14 and 1421 DF, p-value: < 2.2e-16
***Extra Diagnostic information
Residual sum of squares: 8.120609e+14
Sigma(hat): 752522.9
AIC: 42966.76
AICc: 42967.14
BIC: 41731.39
***********************************************************************
* Results of Geographically Weighted Regression *
***********************************************************************
*********************Model calibration information*********************
Kernel function: gaussian
Fixed bandwidth: 971.34
Regression points: the same locations as observations are used.
Distance metric: Euclidean distance metric is used.
****************Summary of GWR coefficient estimates:******************
Min. 1st Qu. Median 3rd Qu.
Intercept -3.5988e+07 -5.1998e+05 7.6780e+05 1.7412e+06
AREA_SQM 1.0003e+03 5.2758e+03 7.4740e+03 1.2301e+04
AGE -1.3475e+05 -2.0813e+04 -8.6260e+03 -3.7784e+03
PROX_CBD -7.7047e+07 -2.3608e+05 -8.3599e+04 3.4646e+04
PROX_CHILDCARE -6.0097e+06 -3.3667e+05 -9.7426e+04 2.9007e+05
PROX_ELDERLYCARE -3.5001e+06 -1.5970e+05 3.1970e+04 1.9577e+05
PROX_URA_GROWTH_AREA -3.0170e+06 -8.2013e+04 7.0749e+04 2.2612e+05
PROX_MRT -3.5282e+06 -6.5836e+05 -1.8833e+05 3.6922e+04
PROX_PARK -1.2062e+06 -2.1732e+05 3.5383e+04 4.1335e+05
PROX_PRIMARY_SCH -2.2695e+07 -1.7066e+05 4.8472e+04 5.1555e+05
PROX_SHOPPING_MALL -7.2585e+06 -1.6684e+05 -1.0517e+04 1.5923e+05
PROX_BUS_STOP -1.4676e+06 -4.5207e+04 3.7601e+05 1.1664e+06
NO_Of_UNITS -1.3170e+03 -2.4822e+02 -3.0846e+01 2.5496e+02
FAMILY_FRIENDLY -2.2749e+06 -1.1140e+05 7.6214e+03 1.6107e+05
FREEHOLD -9.2067e+06 3.8074e+04 1.5169e+05 3.7528e+05
Max.
Intercept 112794435
AREA_SQM 21575
AGE 434203
PROX_CBD 2704604
PROX_CHILDCARE 1654086
PROX_ELDERLYCARE 38867861
PROX_URA_GROWTH_AREA 78515805
PROX_MRT 3124325
PROX_PARK 18122439
PROX_PRIMARY_SCH 4637517
PROX_SHOPPING_MALL 1529953
PROX_BUS_STOP 11342209
NO_Of_UNITS 12907
FAMILY_FRIENDLY 1720745
FREEHOLD 6073642
************************Diagnostic information*************************
Number of data points: 1436
Effective number of parameters (2trace(S) - trace(S'S)): 438.3807
Effective degrees of freedom (n-2trace(S) + trace(S'S)): 997.6193
AICc (GWR book, Fotheringham, et al. 2002, p. 61, eq 2.33): 42263.61
AIC (GWR book, Fotheringham, et al. 2002,GWR p. 96, eq. 4.22): 41632.36
BIC (GWR book, Fotheringham, et al. 2002,GWR p. 61, eq. 2.34): 42515.71
Residual sum of squares: 2.534069e+14
R-square value: 0.8909912
Adjusted R-square value: 0.8430418
***********************************************************************
Program stops at: 2024-10-28 00:10:00.668546 Building Adaptive Bandwidth GWR Model
# compute BW
bw.adaptive <- bw.gwr(formula = SELLING_PRICE ~ AREA_SQM + AGE +
PROX_CBD + PROX_CHILDCARE + PROX_ELDERLYCARE +
PROX_URA_GROWTH_AREA + PROX_MRT + PROX_PARK +
PROX_PRIMARY_SCH + PROX_SHOPPING_MALL + PROX_BUS_STOP +
NO_Of_UNITS + FAMILY_FRIENDLY + FREEHOLD,
data=condo_resale.sp,
approach="CV",
kernel="gaussian",
adaptive=TRUE, # set to true
longlat=FALSE)Adaptive bandwidth: 895 CV score: 7.952401e+14
Adaptive bandwidth: 561 CV score: 7.667364e+14
Adaptive bandwidth: 354 CV score: 6.953454e+14
Adaptive bandwidth: 226 CV score: 6.15223e+14
Adaptive bandwidth: 147 CV score: 5.674373e+14
Adaptive bandwidth: 98 CV score: 5.426745e+14
Adaptive bandwidth: 68 CV score: 5.168117e+14
Adaptive bandwidth: 49 CV score: 4.859631e+14
Adaptive bandwidth: 37 CV score: 4.646518e+14
Adaptive bandwidth: 30 CV score: 4.422088e+14
Adaptive bandwidth: 25 CV score: 4.430816e+14
Adaptive bandwidth: 32 CV score: 4.505602e+14
Adaptive bandwidth: 27 CV score: 4.462172e+14
Adaptive bandwidth: 30 CV score: 4.422088e+14 # calibrate using a.BW GWR model
gwr.adaptive <- gwr.basic(formula = SELLING_PRICE ~ AREA_SQM + AGE +
PROX_CBD + PROX_CHILDCARE + PROX_ELDERLYCARE +
PROX_URA_GROWTH_AREA + PROX_MRT + PROX_PARK +
PROX_PRIMARY_SCH + PROX_SHOPPING_MALL + PROX_BUS_STOP +
NO_Of_UNITS + FAMILY_FRIENDLY + FREEHOLD,
data=condo_resale.sp, bw=bw.adaptive,
kernel = 'gaussian',
adaptive=TRUE,
longlat = FALSE)
gwr.adaptive ***********************************************************************
* Package GWmodel *
***********************************************************************
Program starts at: 2024-10-28 00:10:05.350122
Call:
gwr.basic(formula = SELLING_PRICE ~ AREA_SQM + AGE + PROX_CBD +
PROX_CHILDCARE + PROX_ELDERLYCARE + PROX_URA_GROWTH_AREA +
PROX_MRT + PROX_PARK + PROX_PRIMARY_SCH + PROX_SHOPPING_MALL +
PROX_BUS_STOP + NO_Of_UNITS + FAMILY_FRIENDLY + FREEHOLD,
data = condo_resale.sp, bw = bw.adaptive, kernel = "gaussian",
adaptive = TRUE, longlat = FALSE)
Dependent (y) variable: SELLING_PRICE
Independent variables: AREA_SQM AGE PROX_CBD PROX_CHILDCARE PROX_ELDERLYCARE PROX_URA_GROWTH_AREA PROX_MRT PROX_PARK PROX_PRIMARY_SCH PROX_SHOPPING_MALL PROX_BUS_STOP NO_Of_UNITS FAMILY_FRIENDLY FREEHOLD
Number of data points: 1436
***********************************************************************
* Results of Global Regression *
***********************************************************************
Call:
lm(formula = formula, data = data)
Residuals:
Min 1Q Median 3Q Max
-3470778 -298119 -23481 248917 12234210
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 527633.22 108183.22 4.877 1.20e-06 ***
AREA_SQM 12777.52 367.48 34.771 < 2e-16 ***
AGE -24687.74 2754.84 -8.962 < 2e-16 ***
PROX_CBD -77131.32 5763.12 -13.384 < 2e-16 ***
PROX_CHILDCARE -318472.75 107959.51 -2.950 0.003231 **
PROX_ELDERLYCARE 185575.62 39901.86 4.651 3.61e-06 ***
PROX_URA_GROWTH_AREA 39163.25 11754.83 3.332 0.000885 ***
PROX_MRT -294745.11 56916.37 -5.179 2.56e-07 ***
PROX_PARK 570504.81 65507.03 8.709 < 2e-16 ***
PROX_PRIMARY_SCH 159856.14 60234.60 2.654 0.008046 **
PROX_SHOPPING_MALL -220947.25 36561.83 -6.043 1.93e-09 ***
PROX_BUS_STOP 682482.22 134513.24 5.074 4.42e-07 ***
NO_Of_UNITS -245.48 87.95 -2.791 0.005321 **
FAMILY_FRIENDLY 146307.58 46893.02 3.120 0.001845 **
FREEHOLD 350599.81 48506.48 7.228 7.98e-13 ***
---Significance stars
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 756000 on 1421 degrees of freedom
Multiple R-squared: 0.6507
Adjusted R-squared: 0.6472
F-statistic: 189.1 on 14 and 1421 DF, p-value: < 2.2e-16
***Extra Diagnostic information
Residual sum of squares: 8.120609e+14
Sigma(hat): 752522.9
AIC: 42966.76
AICc: 42967.14
BIC: 41731.39
***********************************************************************
* Results of Geographically Weighted Regression *
***********************************************************************
*********************Model calibration information*********************
Kernel function: gaussian
Adaptive bandwidth: 30 (number of nearest neighbours)
Regression points: the same locations as observations are used.
Distance metric: Euclidean distance metric is used.
****************Summary of GWR coefficient estimates:******************
Min. 1st Qu. Median 3rd Qu.
Intercept -1.3487e+08 -2.4669e+05 7.7928e+05 1.6194e+06
AREA_SQM 3.3188e+03 5.6285e+03 7.7825e+03 1.2738e+04
AGE -9.6746e+04 -2.9288e+04 -1.4043e+04 -5.6119e+03
PROX_CBD -2.5330e+06 -1.6256e+05 -7.7242e+04 2.6624e+03
PROX_CHILDCARE -1.2790e+06 -2.0175e+05 8.7158e+03 3.7778e+05
PROX_ELDERLYCARE -1.6212e+06 -9.2050e+04 6.1029e+04 2.8184e+05
PROX_URA_GROWTH_AREA -7.2686e+06 -3.0350e+04 4.5869e+04 2.4613e+05
PROX_MRT -4.3781e+07 -6.7282e+05 -2.2115e+05 -7.4593e+04
PROX_PARK -2.9020e+06 -1.6782e+05 1.1601e+05 4.6572e+05
PROX_PRIMARY_SCH -8.6418e+05 -1.6627e+05 -7.7853e+03 4.3222e+05
PROX_SHOPPING_MALL -1.8272e+06 -1.3175e+05 -1.4049e+04 1.3799e+05
PROX_BUS_STOP -2.0579e+06 -7.1461e+04 4.1104e+05 1.2071e+06
NO_Of_UNITS -2.1993e+03 -2.3685e+02 -3.4699e+01 1.1657e+02
FAMILY_FRIENDLY -5.9879e+05 -5.0927e+04 2.6173e+04 2.2481e+05
FREEHOLD -1.6340e+05 4.0765e+04 1.9023e+05 3.7960e+05
Max.
Intercept 18758355
AREA_SQM 23064
AGE 13303
PROX_CBD 11346650
PROX_CHILDCARE 2892127
PROX_ELDERLYCARE 2465671
PROX_URA_GROWTH_AREA 7384059
PROX_MRT 1186242
PROX_PARK 2588497
PROX_PRIMARY_SCH 3381462
PROX_SHOPPING_MALL 38038564
PROX_BUS_STOP 12081592
NO_Of_UNITS 1010
FAMILY_FRIENDLY 2072414
FREEHOLD 1813995
************************Diagnostic information*************************
Number of data points: 1436
Effective number of parameters (2trace(S) - trace(S'S)): 350.3088
Effective degrees of freedom (n-2trace(S) + trace(S'S)): 1085.691
AICc (GWR book, Fotheringham, et al. 2002, p. 61, eq 2.33): 41982.22
AIC (GWR book, Fotheringham, et al. 2002,GWR p. 96, eq. 4.22): 41546.74
BIC (GWR book, Fotheringham, et al. 2002,GWR p. 61, eq. 2.34): 41914.08
Residual sum of squares: 2.528227e+14
R-square value: 0.8912425
Adjusted R-square value: 0.8561185
***********************************************************************
Program stops at: 2024-10-28 00:10:06.010656 Visualise GWR Output
Lets break down the loooong output:
Condition Number: computes local collinearity (Results associated with condition numbers > 30, may be unreliable.
Local R2: range between 0.0 and 1.0; indicates how well the local regression model fits observed y values. Very low values indicate the local model is performing poorly. Mapping the Local R2 values to see where GWR predicts well and where it predicts poorly may provide clues about important variables that may be missing from the regression model.
Predicted: estimated (or fitted) y values; computed by GWR.
Residuals: estimated y - observed y. Standardized residuals have a avg=0, stdev = 1. A cold-to-hot rendered map of standardized residuals can be produce by using these values.
Coefficient Standard Error: these values measure the reliability of each coefficient estimate. Confidence in those estimates are higher when standard errors are small in relation to the actual coefficient values. Large standard errors may indicate problems with local collinearity
They are all stored in a SpatialPointsDataFrame or SpatialPolygonsDataFrame object integrated with fit.points, GWR coefficient estimates, y value, predicted values, coefficient standard errors and t-values in its “data” slot in an object called SDF of the output list.
Converting SDF into sf data.frame
condo_resale.sf.adaptive <- st_as_sf(gwr.adaptive$SDF) %>% st_transform(crs=3414)
condo_resale.sf.adaptive.svy21 <- st_transform(condo_resale.sf.adaptive, 3414)
gwr.adaptive.output <- as.data.frame(gwr.adaptive$SDF)
condo_resale.sf.adaptive <- cbind(condo_resale.sf.adaptive.svy21, as.matrix(gwr.adaptive.output))
glimpse(condo_resale.sf.adaptive)Rows: 1,436
Columns: 105
$ Intercept <dbl> 2050011.67, 1633128.24, 3433608.17, 234358.9…
$ AREA_SQM <dbl> 9561.892, 16576.853, 13091.861, 20730.601, 6…
$ AGE <dbl> -9514.634, -58185.479, -26707.386, -93308.98…
$ PROX_CBD <dbl> -120681.94, -149434.22, -259397.77, 2426853.…
$ PROX_CHILDCARE <dbl> 319266.925, 441102.177, -120116.816, 480825.…
$ PROX_ELDERLYCARE <dbl> -393417.795, 325188.741, 535855.806, 314783.…
$ PROX_URA_GROWTH_AREA <dbl> -159980.203, -142290.389, -253621.206, -2679…
$ PROX_MRT <dbl> -299742.96, -2510522.23, -936853.28, -203947…
$ PROX_PARK <dbl> -172104.47, 523379.72, 209099.85, -759153.26…
$ PROX_PRIMARY_SCH <dbl> 242668.03, 1106830.66, 571462.33, 3127477.21…
$ PROX_SHOPPING_MALL <dbl> 300881.390, -87693.378, -126732.712, -29593.…
$ PROX_BUS_STOP <dbl> 1210615.44, 1843587.22, 1411924.90, 7225577.…
$ NO_Of_UNITS <dbl> 104.8290640, -288.3441183, -9.5532945, -161.…
$ FAMILY_FRIENDLY <dbl> -9075.370, 310074.664, 5949.746, 1556178.531…
$ FREEHOLD <dbl> 303955.61, 396221.27, 168821.75, 1212515.58,…
$ y <dbl> 3000000, 3880000, 3325000, 4250000, 1400000,…
$ yhat <dbl> 2886531.8, 3466801.5, 3616527.2, 5435481.6, …
$ residual <dbl> 113468.16, 413198.52, -291527.20, -1185481.6…
$ CV_Score <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Stud_residual <dbl> 0.38207013, 1.01433140, -0.83780678, -2.8461…
$ Intercept_SE <dbl> 516105.5, 488083.5, 963711.4, 444185.5, 2119…
$ AREA_SQM_SE <dbl> 823.2860, 825.2380, 988.2240, 617.4007, 1376…
$ AGE_SE <dbl> 5889.782, 6226.916, 6510.236, 6010.511, 8180…
$ PROX_CBD_SE <dbl> 37411.22, 23615.06, 56103.77, 469337.41, 410…
$ PROX_CHILDCARE_SE <dbl> 319111.1, 299705.3, 349128.5, 304965.2, 6987…
$ PROX_ELDERLYCARE_SE <dbl> 120633.34, 84546.69, 129687.07, 127150.69, 3…
$ PROX_URA_GROWTH_AREA_SE <dbl> 56207.39, 76956.50, 95774.60, 470762.12, 474…
$ PROX_MRT_SE <dbl> 185181.3, 281133.9, 275483.7, 279877.1, 3638…
$ PROX_PARK_SE <dbl> 205499.6, 229358.7, 314124.3, 227249.4, 3645…
$ PROX_PRIMARY_SCH_SE <dbl> 152400.7, 165150.7, 196662.6, 240878.9, 2490…
$ PROX_SHOPPING_MALL_SE <dbl> 109268.8, 98906.8, 119913.3, 177104.1, 30103…
$ PROX_BUS_STOP_SE <dbl> 600668.6, 410222.1, 464156.7, 562810.8, 7409…
$ NO_Of_UNITS_SE <dbl> 218.1258, 208.9410, 210.9828, 361.7767, 299.…
$ FAMILY_FRIENDLY_SE <dbl> 131474.73, 114989.07, 146607.22, 108726.62, …
$ FREEHOLD_SE <dbl> 115954.0, 130110.0, 141031.5, 138239.1, 2106…
$ Intercept_TV <dbl> 3.9720784, 3.3460017, 3.5629010, 0.5276150, …
$ AREA_SQM_TV <dbl> 11.614302, 20.087361, 13.247868, 33.577223, …
$ AGE_TV <dbl> -1.6154474, -9.3441881, -4.1023685, -15.5243…
$ PROX_CBD_TV <dbl> -3.22582173, -6.32792021, -4.62353528, 5.170…
$ PROX_CHILDCARE_TV <dbl> 1.000488185, 1.471786337, -0.344047555, 1.57…
$ PROX_ELDERLYCARE_TV <dbl> -3.26126929, 3.84626245, 4.13191383, 2.47567…
$ PROX_URA_GROWTH_AREA_TV <dbl> -2.846248368, -1.848971738, -2.648105057, -5…
$ PROX_MRT_TV <dbl> -1.61864578, -8.92998600, -3.40075727, -7.28…
$ PROX_PARK_TV <dbl> -0.83749312, 2.28192684, 0.66565951, -3.3406…
$ PROX_PRIMARY_SCH_TV <dbl> 1.59230221, 6.70194543, 2.90580089, 12.98361…
$ PROX_SHOPPING_MALL_TV <dbl> 2.753588422, -0.886626400, -1.056869486, -0.…
$ PROX_BUS_STOP_TV <dbl> 2.0154464, 4.4941192, 3.0419145, 12.8383775,…
$ NO_Of_UNITS_TV <dbl> 0.480589953, -1.380026395, -0.045279967, -0.…
$ FAMILY_FRIENDLY_TV <dbl> -0.06902748, 2.69655779, 0.04058290, 14.3127…
$ FREEHOLD_TV <dbl> 2.6213469, 3.0452799, 1.1970499, 8.7711485, …
$ Local_R2 <dbl> 0.8846744, 0.8899773, 0.8947007, 0.9073605, …
$ Intercept.1 <dbl> 2050011.67, 1633128.24, 3433608.17, 234358.9…
$ AREA_SQM.1 <dbl> 9561.892, 16576.853, 13091.861, 20730.601, 6…
$ AGE.1 <dbl> -9514.634, -58185.479, -26707.386, -93308.98…
$ PROX_CBD.1 <dbl> -120681.94, -149434.22, -259397.77, 2426853.…
$ PROX_CHILDCARE.1 <dbl> 319266.925, 441102.177, -120116.816, 480825.…
$ PROX_ELDERLYCARE.1 <dbl> -393417.795, 325188.741, 535855.806, 314783.…
$ PROX_URA_GROWTH_AREA.1 <dbl> -159980.203, -142290.389, -253621.206, -2679…
$ PROX_MRT.1 <dbl> -299742.96, -2510522.23, -936853.28, -203947…
$ PROX_PARK.1 <dbl> -172104.47, 523379.72, 209099.85, -759153.26…
$ PROX_PRIMARY_SCH.1 <dbl> 242668.03, 1106830.66, 571462.33, 3127477.21…
$ PROX_SHOPPING_MALL.1 <dbl> 300881.390, -87693.378, -126732.712, -29593.…
$ PROX_BUS_STOP.1 <dbl> 1210615.44, 1843587.22, 1411924.90, 7225577.…
$ NO_Of_UNITS.1 <dbl> 104.8290640, -288.3441183, -9.5532945, -161.…
$ FAMILY_FRIENDLY.1 <dbl> -9075.370, 310074.664, 5949.746, 1556178.531…
$ FREEHOLD.1 <dbl> 303955.61, 396221.27, 168821.75, 1212515.58,…
$ y.1 <dbl> 3000000, 3880000, 3325000, 4250000, 1400000,…
$ yhat.1 <dbl> 2886531.8, 3466801.5, 3616527.2, 5435481.6, …
$ residual.1 <dbl> 113468.16, 413198.52, -291527.20, -1185481.6…
$ CV_Score.1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Stud_residual.1 <dbl> 0.38207013, 1.01433140, -0.83780678, -2.8461…
$ Intercept_SE.1 <dbl> 516105.5, 488083.5, 963711.4, 444185.5, 2119…
$ AREA_SQM_SE.1 <dbl> 823.2860, 825.2380, 988.2240, 617.4007, 1376…
$ AGE_SE.1 <dbl> 5889.782, 6226.916, 6510.236, 6010.511, 8180…
$ PROX_CBD_SE.1 <dbl> 37411.22, 23615.06, 56103.77, 469337.41, 410…
$ PROX_CHILDCARE_SE.1 <dbl> 319111.1, 299705.3, 349128.5, 304965.2, 6987…
$ PROX_ELDERLYCARE_SE.1 <dbl> 120633.34, 84546.69, 129687.07, 127150.69, 3…
$ PROX_URA_GROWTH_AREA_SE.1 <dbl> 56207.39, 76956.50, 95774.60, 470762.12, 474…
$ PROX_MRT_SE.1 <dbl> 185181.3, 281133.9, 275483.7, 279877.1, 3638…
$ PROX_PARK_SE.1 <dbl> 205499.6, 229358.7, 314124.3, 227249.4, 3645…
$ PROX_PRIMARY_SCH_SE.1 <dbl> 152400.7, 165150.7, 196662.6, 240878.9, 2490…
$ PROX_SHOPPING_MALL_SE.1 <dbl> 109268.8, 98906.8, 119913.3, 177104.1, 30103…
$ PROX_BUS_STOP_SE.1 <dbl> 600668.6, 410222.1, 464156.7, 562810.8, 7409…
$ NO_Of_UNITS_SE.1 <dbl> 218.1258, 208.9410, 210.9828, 361.7767, 299.…
$ FAMILY_FRIENDLY_SE.1 <dbl> 131474.73, 114989.07, 146607.22, 108726.62, …
$ FREEHOLD_SE.1 <dbl> 115954.0, 130110.0, 141031.5, 138239.1, 2106…
$ Intercept_TV.1 <dbl> 3.9720784, 3.3460017, 3.5629010, 0.5276150, …
$ AREA_SQM_TV.1 <dbl> 11.614302, 20.087361, 13.247868, 33.577223, …
$ AGE_TV.1 <dbl> -1.6154474, -9.3441881, -4.1023685, -15.5243…
$ PROX_CBD_TV.1 <dbl> -3.22582173, -6.32792021, -4.62353528, 5.170…
$ PROX_CHILDCARE_TV.1 <dbl> 1.000488185, 1.471786337, -0.344047555, 1.57…
$ PROX_ELDERLYCARE_TV.1 <dbl> -3.26126929, 3.84626245, 4.13191383, 2.47567…
$ PROX_URA_GROWTH_AREA_TV.1 <dbl> -2.846248368, -1.848971738, -2.648105057, -5…
$ PROX_MRT_TV.1 <dbl> -1.61864578, -8.92998600, -3.40075727, -7.28…
$ PROX_PARK_TV.1 <dbl> -0.83749312, 2.28192684, 0.66565951, -3.3406…
$ PROX_PRIMARY_SCH_TV.1 <dbl> 1.59230221, 6.70194543, 2.90580089, 12.98361…
$ PROX_SHOPPING_MALL_TV.1 <dbl> 2.753588422, -0.886626400, -1.056869486, -0.…
$ PROX_BUS_STOP_TV.1 <dbl> 2.0154464, 4.4941192, 3.0419145, 12.8383775,…
$ NO_Of_UNITS_TV.1 <dbl> 0.480589953, -1.380026395, -0.045279967, -0.…
$ FAMILY_FRIENDLY_TV.1 <dbl> -0.06902748, 2.69655779, 0.04058290, 14.3127…
$ FREEHOLD_TV.1 <dbl> 2.6213469, 3.0452799, 1.1970499, 8.7711485, …
$ Local_R2.1 <dbl> 0.8846744, 0.8899773, 0.8947007, 0.9073605, …
$ coords.x1 <dbl> 22085.12, 25656.84, 23963.99, 27044.28, 4104…
$ coords.x2 <dbl> 29951.54, 34546.20, 32890.80, 32319.77, 3374…
$ geometry <POINT [m]> POINT (22085.12 29951.54), POINT (2565…summary(gwr.adaptive$SDF$yhat) Min. 1st Qu. Median Mean 3rd Qu. Max.
171347 1102001 1385528 1751842 1982307 13887901 Visualise local R2
Code
tmap_mode("view")tmap mode set to interactive viewingCode
tmap_options(check.and.fix = TRUE)
tm_shape(mpsz_3414) +
tm_polygons(alpha = 0.1) +
tm_shape(condo_resale.sf.adaptive) +
tm_dots(col = "Local_R2",
border.col = "gray60",
border.lwd = 1) +
tm_view(set.zoom.limits = c(11,14))Warning: The shape mpsz_3414 is invalid (after reprojection). See
sf::st_is_validtmap_mode("plot")tmap mode set to plottingVisualise coefficient estimates
Code
tmap_mode("view")tmap mode set to interactive viewingCode
tmap_options(check.and.fix = TRUE)
AREA_SQM_SE <- tm_shape(mpsz_3414)+
tm_polygons(alpha = 0.1) +
tm_shape(condo_resale.sf.adaptive) +
tm_dots(col = "AREA_SQM_SE",
border.col = "gray60",
border.lwd = 1) +
tm_view(set.zoom.limits = c(11,14))
AREA_SQM_TV <- tm_shape(mpsz_3414)+
tm_polygons(alpha = 0.1) +
tm_shape(condo_resale.sf.adaptive) +
tm_dots(col = "AREA_SQM_TV",
border.col = "gray60",
border.lwd = 1) +
tm_view(set.zoom.limits = c(11,14))
tmap_arrange(AREA_SQM_SE, AREA_SQM_TV, asp=1, ncol=2, sync = TRUE)Warning: The shape mpsz_3414 is invalid (after reprojection). See
sf::st_is_valid
Warning: The shape mpsz_3414 is invalid (after reprojection). See
sf::st_is_validTo view by region,
Code
tmap_mode("plot")tmap mode set to plottingCode
tmap_options(check.and.fix = TRUE)
tm_shape(mpsz_3414[mpsz_3414$REGION_N=="CENTRAL REGION", ])+
tm_polygons()+
tm_shape(condo_resale.sf.adaptive) +
tm_bubbles(col = "Local_R2",
size = 0.15,
border.col = "gray60",
border.lwd = 1)Warning: The shape mpsz_3414[mpsz_3414$REGION_N == "CENTRAL REGION", ] is
invalid. See sf::st_is_valid